In recent years, optical computing techniques have been developed for applications in the oil and gas industry in the form of optical sensors on downhole or surface equipment to evaluate a variety of fluid properties. In general, optical computing devices, also commonly referred to as “opticoanalytical devices,” can be used to analyze and monitor a sample substance in real time. Such optical computing devices will often employ a light source that emits electromagnetic radiation that either reflects from or is transmitted through the sample and optically interacts with an optical processing element to determine quantitative and/or qualitative values of one or more physical or chemical properties of the substance being analyzed. The optical processing element may be, for example, an ICE. One type of an ICE is an optical thin film interference device, also known as a multivariate optical element (“MOE”). Each ICE can be designed to operate over a continuum of wavelengths in the electromagnetic spectrum from the UV to mid-infrared (MIR) ranges, or any sub-set of that region. Electromagnetic radiation that optically interacts with the sample substance is changed and processed by the ICE so as to be measured by a detector. The output of the detector is then correlated to a physical or chemical property of the substance being monitored.
Fundamentally, optical computing devices utilize optical elements to perform calculations, as opposed to the hardwired circuits of conventional electronic processors. When light from a light source interacts with a substance, unique physical and chemical information about the substance is encoded in the electromagnetic radiation that is reflected from, transmitted through, or radiated from the sample. Thus, the optical computing device, through use of the ICE and one or more detectors, is capable of extracting the information of one or multiple characteristics/analytes within a substance and converting that information into a detectable output signal reflecting the overall properties of a sample. Such characteristics may include, for example, the presence of certain elements, compositions, fluid phases, etc. existing within the substance.
Currently, ICEs are assessed by applying an ICE regression vector to a single set of calibration data (i.e., spectral data set) to evaluate a performance factor such as, for example, standard error of calibration (“SEC”). This procedure is performed on a set of spectral data that describes a single chemical system that contains one or more components: its target characteristic/analyte and the remaining components (including spectral interferents), usually referred to the matrix. A subset of the chemical system can be used for validation purposes to calculate the standard error of prediction (“SEP”); this subset represents the same chemical system and the calibration set. An illustrative ICE (e.g., MOE), which may consist of a series of alternating layers of high and low refractive index materials deposited onto an optical substrate which has a transmission function (T), is designed by assessing the performance factor, for example SEC, and using a minimization function to adjust the layers to make an ICE with a low SEC, which is thus as predictive as possible. Accordingly, the ICE is predictive for only one sample characteristic.
In some cases, measurements of more than one characteristic of a substance are needed. This is accomplished by either multiple optical computing systems (each with its own ICE), or a single larger optical computing system with a plurality of ICEs (each measuring a single characteristic separately). However, is some cases (like downhole reservoir fluid characterization), there are space and size requirements that prohibit multiple or large optical computing systems. Accordingly, there is a need in the art for an ICE which is predictive for multiple sample characteristics, thereby minimizing the number of ICEs required to measure the plurality of characteristics.